Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images. (October 2021)

• Record Type: Journal Article
• Authors: Pathan, Sameena; Siddalingaswamy, P.C.; Kumar, Preetham; Pai M M, Manohara; Ali, Tanweer; Acharya, U. Rajendra
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: BGWO -- Covid-19 -- CNN -- DST -- Ensemble -- GWO -- WOA -- Hyperparameters; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104835
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application. Highlights: A novel ensemble architecture built using CNN and dynamic selection techniques is proposed for detection of covid-19. The CNN hyperparameters are optimized using WOA-BAT optimization techniques. Limited data problem is solved by using AdaBoost of decision stump trees. A classification accuracy of 96% is obtained in detection of covid-19 using Chest CT scans.